
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


ins_data = pd.read_csv('https://raw.githubusercontent.com/FlipRoboTechnologies/ML_-Datasets/main/Insurance%20Claim%20Fraud%20Detection/Automobile_insurance_fraud.csv')
ins_data


ins_data.shape

(999, 39)


ins_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 999 entries, 0 to 998
Data columns (total 39 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   328                       999 non-null    int64  
 1   48                        999 non-null    int64  
 2   521585                    999 non-null    int64  
 3   17-10-2014                999 non-null    object 
 4   OH                        999 non-null    object 
 5   250/500                   999 non-null    object 
 6   1000                      999 non-null    int64  
 7   1406.91                   999 non-null    float64
 8   0                         999 non-null    int64  
 9   466132                    999 non-null    int64  
 10  MALE                      999 non-null    object 
 11  MD                        999 non-null    object 
 12  craft-repair              999 non-null    object 
 13  sleeping                  999 non-null    object 
 14  husband                   999 non-null    object 
 15  53300                     999 non-null    int64  
 16  0.1                       999 non-null    int64  
 17  25-01-2015                999 non-null    object 
 18  Single Vehicle Collision  999 non-null    object 
 19  Side Collision            999 non-null    object 
 20  Major Damage              999 non-null    object 
 21  Police                    908 non-null    object 
 22  SC                        999 non-null    object 
 23  Columbus                  999 non-null    object 
 24  9935 4th Drive            999 non-null    object 
 25  5                         999 non-null    int64  
 26  1                         999 non-null    int64  
 27  YES                       999 non-null    object 
 28  1.1                       999 non-null    int64  
 29  2                         999 non-null    int64  
 30  YES.1                     999 non-null    object 
 31  71610                     999 non-null    int64  
 32  6510                      999 non-null    int64  
 33  13020                     999 non-null    int64  
 34  52080                     999 non-null    int64  
 35  Saab                      999 non-null    object 
 36  92x                       999 non-null    object 
 37  2004                      999 non-null    int64  
 38  Y                         999 non-null    object 
dtypes: float64(1), int64(17), object(21)
memory usage: 304.5+ KB


columns = ins_data.columns
print(columns)

Index(['328', '48', '521585', '17-10-2014', 'OH', '250/500', '1000', '1406.91',
       '0', '466132', 'MALE', 'MD', 'craft-repair', 'sleeping', 'husband',
       '53300', '0.1', '25-01-2015', 'Single Vehicle Collision',
       'Side Collision', 'Major Damage', 'Police', 'SC', 'Columbus',
       '9935 4th Drive', '5', '1', 'YES', '1.1', '2', 'YES.1', '71610', '6510',
       '13020', '52080', 'Saab', '92x', '2004', 'Y'],
      dtype='object')


# Create a dictionary mapping old column names to new descriptive names
column_mapping = {
    '328': 'months_as_customer',
    '48': 'age',
    '521585': 'policy_number',
    '17-10-2014': 'policy_bind_date',
    'OH': 'policy_state',
    '250/500': 'policy_csl',
    '1000': 'policy_deductible',
    '1406.91': 'policy_annual_premium',
    '0': 'umbrella_limit',
    '466132': 'insured_zip',
    'MALE': 'insured_sex',
    'MD': 'insured_education_level',
    'craft-repair': 'insured_occupation',
    'sleeping': 'insured_hobbies',
    'husband': 'insured_relationship',
    '53300': 'capital-gains',
    '0.1': 'capital-loss',
    '25-01-2015': 'incident_date',
    'Single Vehicle Collision': 'incident_type',
    'Side Collision': 'collision_type',
    'Major Damage': 'incident_severity',
    'Police': 'authorities_contacted',
    'SC': 'incident_state',
    'Columbus': 'incident_city',
    '9935 4th Drive': 'incident_location',
    '5': 'incident_hour_of_the_day',
    '1': 'number_of_vehicles_involved',
    'YES': 'property_damage',
    '1.1': 'bodily_injuries',
    '2': 'witnesses',
    'YES.1': 'police_report_available',
    '71610': 'total_claim_amount',
    '6510': 'injury_claim',
    '13020': 'property_claim',
    '52080': 'vehicle_claim',
    'Saab': 'auto_make',
    '92x': 'auto_model',
    '2004': 'auto_year',
    'Y': 'fraud_reported'
}

# Step 1: Add the current column names as the first row of the DataFrame
ins_data.loc[-1] = ins_data.columns
ins_data.index = ins_data.index + 1  # Shift the index
ins_data = ins_data.sort_index()  # Sort the DataFrame to put the new row at the top

# Step 2: Rename the columns using the column_mapping dictionary
ins_data.rename(columns=column_mapping, inplace=True)

# Display the updated DataFrame
print(ins_data.head())

  months_as_customer age policy_number policy_bind_date policy_state  \
0                328  48        521585       17-10-2014           OH   
1                228  42        342868       27-06-2006           IN   
2                134  29        687698       06-09-2000           OH   
3                256  41        227811       25-05-1990           IL   
4                228  44        367455       06-06-2014           IL   

  policy_csl policy_deductible policy_annual_premium umbrella_limit  \
0    250/500              1000               1406.91              0   
1    250/500              2000               1197.22        5000000   
2    100/300              2000               1413.14        5000000   
3    250/500              2000               1415.74        6000000   
4   500/1000              1000               1583.91        6000000   

  insured_zip  ... witnesses police_report_available total_claim_amount  \
0      466132  ...         2                   YES.1              71610   
1      468176  ...         0                       ?               5070   
2      430632  ...         3                      NO              34650   
3      608117  ...         2                      NO              63400   
4      610706  ...         1                      NO               6500   

  injury_claim property_claim vehicle_claim  auto_make auto_model auto_year  \
0         6510          13020         52080       Saab        92x      2004   
1          780            780          3510   Mercedes       E400      2007   
2         7700           3850         23100      Dodge        RAM      2007   
3         6340           6340         50720  Chevrolet      Tahoe      2014   
4         1300            650          4550     Accura        RSX      2009   

  fraud_reported  
0              Y  
1              Y  
2              N  
3              Y  
4              N  

[5 rows x 39 columns]


# ins_data.loc[-1] = ins_data.columns
# ins_data.index = ins_data.index + 1  # shifting index
# ins_data = ins_data.sort_index()

# # Step 2: Replace the column names with new names
# new_column_names = [f'Column_{i}' for i in range(1, len(ins_data.columns) + 1)]
# ins_data.columns = new_column_names

# print(ins_data)

    Column_1 Column_2 Column_3    Column_4 Column_5  Column_6 Column_7  \
0        328       48   521585  17-10-2014       OH   250/500     1000   
1        228       42   342868  27-06-2006       IN   250/500     2000   
2        134       29   687698  06-09-2000       OH   100/300     2000   
3        256       41   227811  25-05-1990       IL   250/500     2000   
4        228       44   367455  06-06-2014       IL  500/1000     1000   
..       ...      ...      ...         ...      ...       ...      ...   
995        3       38   941851  16-07-1991       OH  500/1000     1000   
996      285       41   186934  05-01-2014       IL   100/300     1000   
997      130       34   918516  17-02-2003       OH   250/500      500   
998      458       62   533940  18-11-2011       IL  500/1000     2000   
999      456       60   556080  11-11-1996       OH   250/500     1000   

    Column_8 Column_9 Column_10  ... Column_30 Column_31 Column_32 Column_33  \
0    1406.91        0    466132  ...         2     YES.1     71610      6510   
1    1197.22  5000000    468176  ...         0         ?      5070       780   
2    1413.14  5000000    430632  ...         3        NO     34650      7700   
3    1415.74  6000000    608117  ...         2        NO     63400      6340   
4    1583.91  6000000    610706  ...         1        NO      6500      1300   
..       ...      ...       ...  ...       ...       ...       ...       ...   
995   1310.8        0    431289  ...         1         ?     87200     17440   
996  1436.79        0    608177  ...         3         ?    108480     18080   
997  1383.49  3000000    442797  ...         3       YES     67500      7500   
998  1356.92  5000000    441714  ...         1       YES     46980      5220   
999   766.19        0    612260  ...         3         ?      5060       460   

    Column_34 Column_35   Column_36 Column_37 Column_38 Column_39  
0       13020     52080        Saab       92x      2004         Y  
1         780      3510    Mercedes      E400      2007         Y  
2        3850     23100       Dodge       RAM      2007         N  
3        6340     50720   Chevrolet     Tahoe      2014         Y  
4         650      4550      Accura       RSX      2009         N  
..        ...       ...         ...       ...       ...       ...  
995      8720     61040       Honda    Accord      2006         N  
996     18080     72320  Volkswagen    Passat      2015         N  
997      7500     52500      Suburu   Impreza      1996         N  
998      5220     36540        Audi        A5      1998         N  
999       920      3680    Mercedes      E400      2007         N  

[1000 rows x 39 columns]


ins_data.columns

Index(['months_as_customer', 'age', 'policy_number', 'policy_bind_date',
       'policy_state', 'policy_csl', 'policy_deductible',
       'policy_annual_premium', 'umbrella_limit', 'insured_zip', 'insured_sex',
       'insured_education_level', 'insured_occupation', 'insured_hobbies',
       'insured_relationship', 'capital-gains', 'capital-loss',
       'incident_date', 'incident_type', 'collision_type', 'incident_severity',
       'authorities_contacted', 'incident_state', 'incident_city',
       'incident_location', 'incident_hour_of_the_day',
       'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
       'witnesses', 'police_report_available', 'total_claim_amount',
       'injury_claim', 'property_claim', 'vehicle_claim', 'auto_make',
       'auto_model', 'auto_year', 'fraud_reported'],
      dtype='object')


ins_data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1000 entries, 0 to 999
Data columns (total 39 columns):
 #   Column                       Non-Null Count  Dtype 
---  ------                       --------------  ----- 
 0   months_as_customer           1000 non-null   object
 1   age                          1000 non-null   object
 2   policy_number                1000 non-null   object
 3   policy_bind_date             1000 non-null   object
 4   policy_state                 1000 non-null   object
 5   policy_csl                   1000 non-null   object
 6   policy_deductible            1000 non-null   object
 7   policy_annual_premium        1000 non-null   object
 8   umbrella_limit               1000 non-null   object
 9   insured_zip                  1000 non-null   object
 10  insured_sex                  1000 non-null   object
 11  insured_education_level      1000 non-null   object
 12  insured_occupation           1000 non-null   object
 13  insured_hobbies              1000 non-null   object
 14  insured_relationship         1000 non-null   object
 15  capital-gains                1000 non-null   object
 16  capital-loss                 1000 non-null   object
 17  incident_date                1000 non-null   object
 18  incident_type                1000 non-null   object
 19  collision_type               1000 non-null   object
 20  incident_severity            1000 non-null   object
 21  authorities_contacted        909 non-null    object
 22  incident_state               1000 non-null   object
 23  incident_city                1000 non-null   object
 24  incident_location            1000 non-null   object
 25  incident_hour_of_the_day     1000 non-null   object
 26  number_of_vehicles_involved  1000 non-null   object
 27  property_damage              1000 non-null   object
 28  bodily_injuries              1000 non-null   object
 29  witnesses                    1000 non-null   object
 30  police_report_available      1000 non-null   object
 31  total_claim_amount           1000 non-null   object
 32  injury_claim                 1000 non-null   object
 33  property_claim               1000 non-null   object
 34  vehicle_claim                1000 non-null   object
 35  auto_make                    1000 non-null   object
 36  auto_model                   1000 non-null   object
 37  auto_year                    1000 non-null   object
 38  fraud_reported               1000 non-null   object
dtypes: object(39)
memory usage: 312.5+ KB


ins_data.columns.to_list()

['months_as_customer',
 'age',
 'policy_number',
 'policy_bind_date',
 'policy_state',
 'policy_csl',
 'policy_deductible',
 'policy_annual_premium',
 'umbrella_limit',
 'insured_zip',
 'insured_sex',
 'insured_education_level',
 'insured_occupation',
 'insured_hobbies',
 'insured_relationship',
 'capital-gains',
 'capital-loss',
 'incident_date',
 'incident_type',
 'collision_type',
 'incident_severity',
 'authorities_contacted',
 'incident_state',
 'incident_city',
 'incident_location',
 'incident_hour_of_the_day',
 'number_of_vehicles_involved',
 'property_damage',
 'bodily_injuries',
 'witnesses',
 'police_report_available',
 'total_claim_amount',
 'injury_claim',
 'property_claim',
 'vehicle_claim',
 'auto_make',
 'auto_model',
 'auto_year',
 'fraud_reported']


ins_data.isnull().sum()

months_as_customer              0
age                             0
policy_number                   0
policy_bind_date                0
policy_state                    0
policy_csl                      0
policy_deductible               0
policy_annual_premium           0
umbrella_limit                  0
insured_zip                     0
insured_sex                     0
insured_education_level         0
insured_occupation              0
insured_hobbies                 0
insured_relationship            0
capital-gains                   0
capital-loss                    0
incident_date                   0
incident_type                   0
collision_type                  0
incident_severity               0
authorities_contacted          91
incident_state                  0
incident_city                   0
incident_location               0
incident_hour_of_the_day        0
number_of_vehicles_involved     0
property_damage                 0
bodily_injuries                 0
witnesses                       0
police_report_available         0
total_claim_amount              0
injury_claim                    0
property_claim                  0
vehicle_claim                   0
auto_make                       0
auto_model                      0
auto_year                       0
fraud_reported                  0
dtype: int64


ins_data.fillna(ins_data['authorities_contacted'].mode()[0] , inplace = True)


ins_data.isnull().sum()

months_as_customer             0
age                            0
policy_number                  0
policy_bind_date               0
policy_state                   0
policy_csl                     0
policy_deductible              0
policy_annual_premium          0
umbrella_limit                 0
insured_zip                    0
insured_sex                    0
insured_education_level        0
insured_occupation             0
insured_hobbies                0
insured_relationship           0
capital-gains                  0
capital-loss                   0
incident_date                  0
incident_type                  0
collision_type                 0
incident_severity              0
authorities_contacted          0
incident_state                 0
incident_city                  0
incident_location              0
incident_hour_of_the_day       0
number_of_vehicles_involved    0
property_damage                0
bodily_injuries                0
witnesses                      0
police_report_available        0
total_claim_amount             0
injury_claim                   0
property_claim                 0
vehicle_claim                  0
auto_make                      0
auto_model                     0
auto_year                      0
fraud_reported                 0
dtype: int64


# Dropping those columns , which is not in use.

ins_data.drop(columns = ['policy_number' , 'incident_location'] , axis = 1 , inplace = True)


ins_data.shape

(1000, 37)


for i in ins_data.columns :
  print(ins_data[i].value_counts())
  print("\n")

months_as_customer
194    8
128    7
210    7
140    7
285    7
      ..
467    1
414    1
352    1
347    1
17     1
Name: count, Length: 392, dtype: int64


age
43    49
39    48
41    45
34    44
30    42
38    42
31    42
37    41
33    39
40    38
32    38
29    35
46    33
36    32
42    32
44    32
35    32
28    30
45    26
26    26
27    24
48    24
47    24
57    16
55    14
25    14
49    14
50    13
53    13
61    10
54    10
24    10
60     9
51     9
56     8
58     8
23     7
21     6
59     5
62     4
52     4
64     2
63     2
20     1
48     1
19     1
22     1
Name: count, dtype: int64


policy_bind_date
01-01-2006    3
28-04-1992    3
05-08-1992    3
14-12-1991    2
09-08-2004    2
             ..
03-06-2014    1
12-12-1998    1
18-02-1999    1
30-10-1997    1
11-11-1996    1
Name: count, Length: 951, dtype: int64


policy_state
OH    352
IL    338
IN    310
Name: count, dtype: int64


policy_csl
250/500     351
100/300     349
500/1000    300
Name: count, dtype: int64


policy_deductible
1000    350
500     342
2000    307
1000      1
Name: count, dtype: int64


policy_annual_premium
1558.29    2
1215.36    2
1362.87    2
1073.83    2
1389.13    2
          ..
1085.03    1
1437.33    1
988.29     1
1238.89    1
766.19     1
Name: count, Length: 991, dtype: int64


umbrella_limit
0           797
6000000      57
5000000      46
4000000      39
7000000      29
3000000      12
8000000       8
9000000       5
2000000       3
10000000      2
0             1
-1000000      1
Name: count, dtype: int64


insured_zip
477695    2
469429    2
446895    2
431202    2
456602    2
         ..
476303    1
450339    1
476502    1
600561    1
612260    1
Name: count, Length: 995, dtype: int64


insured_sex
FEMALE    537
MALE      463
Name: count, dtype: int64


insured_education_level
JD             161
High School    160
Associate      145
MD             144
Masters        143
PhD            125
College        122
Name: count, dtype: int64


insured_occupation
machine-op-inspct    93
prof-specialty       85
tech-support         78
sales                76
exec-managerial      76
craft-repair         74
transport-moving     72
other-service        71
priv-house-serv      71
armed-forces         69
adm-clerical         65
protective-serv      63
handlers-cleaners    54
farming-fishing      53
Name: count, dtype: int64


insured_hobbies
reading           64
exercise          57
paintball         57
bungie-jumping    56
movies            55
golf              55
camping           55
kayaking          54
yachting          53
hiking            52
video-games       50
skydiving         49
base-jumping      49
board-games       48
polo              47
chess             46
dancing           43
sleeping          41
cross-fit         35
basketball        34
Name: count, dtype: int64


insured_relationship
own-child         183
other-relative    177
not-in-family     174
husband           170
wife              155
unmarried         141
Name: count, dtype: int64


capital-gains
0        508
46300      5
51500      4
68500      4
47600      3
        ... 
36700      1
54900      1
69200      1
48800      1
50300      1
Name: count, Length: 339, dtype: int64


capital-loss
 0        474
-53700      5
-31700      5
-50300      5
-45300      4
         ... 
-64100      1
-50400      1
-29900      1
-91400      1
-82100      1
Name: count, Length: 355, dtype: int64


incident_date
02-02-2015    28
17-02-2015    26
07-01-2015    25
10-01-2015    24
04-02-2015    24
24-01-2015    24
19-01-2015    23
08-01-2015    22
13-01-2015    21
30-01-2015    21
12-02-2015    20
22-02-2015    20
31-01-2015    20
06-02-2015    20
21-02-2015    19
01-01-2015    19
23-02-2015    19
12-01-2015    19
14-01-2015    19
21-01-2015    19
03-01-2015    18
14-02-2015    18
01-02-2015    18
28-02-2015    18
20-01-2015    18
18-01-2015    18
25-02-2015    18
06-01-2015    17
09-01-2015    17
08-02-2015    17
24-02-2015    17
26-02-2015    17
13-02-2015    16
15-02-2015    16
16-02-2015    16
05-02-2015    16
16-01-2015    16
17-01-2015    15
18-02-2015    15
28-01-2015    15
15-01-2015    15
22-01-2015    14
20-02-2015    14
27-02-2015    14
23-01-2015    13
03-02-2015    13
27-01-2015    13
09-02-2015    13
04-01-2015    12
01-03-2015    12
26-01-2015    11
29-01-2015    11
02-01-2015    11
19-02-2015    10
11-02-2015    10
10-02-2015    10
07-02-2015    10
25-01-2015    10
11-01-2015     9
05-01-2015     7
Name: count, dtype: int64


incident_type
Multi-vehicle Collision     419
Single Vehicle Collision    403
Vehicle Theft                94
Parked Car                   84
Name: count, dtype: int64


collision_type
Rear Collision     292
Side Collision     276
Front Collision    254
?                  178
Name: count, dtype: int64


incident_severity
Minor Damage      354
Total Loss        280
Major Damage      276
Trivial Damage     90
Name: count, dtype: int64


authorities_contacted
Police       383
Fire         223
Other        198
Ambulance    196
Name: count, dtype: int64


incident_state
NY    262
SC    248
WV    217
VA    110
NC    110
PA     30
OH     23
Name: count, dtype: int64


incident_city
Springfield    157
Arlington      152
Columbus       149
Northbend      145
Hillsdale      141
Riverwood      134
Northbrook     122
Name: count, dtype: int64


incident_hour_of_the_day
17    54
3     53
0     52
23    51
16    49
10    46
4     46
13    46
6     44
14    43
9     43
21    42
18    41
12    40
19    40
7     40
15    39
22    38
8     36
20    34
5     32
2     31
11    30
1     29
5      1
Name: count, dtype: int64


number_of_vehicles_involved
1    580
3    358
4     31
2     30
1      1
Name: count, dtype: int64


property_damage
?      360
NO     338
YES    302
Name: count, dtype: int64


bodily_injuries
0      340
2      332
1      327
1.1      1
Name: count, dtype: int64


witnesses
1    258
0    249
2    249
3    243
2      1
Name: count, dtype: int64


police_report_available
?        343
NO       343
YES      313
YES.1      1
Name: count, dtype: int64


total_claim_amount
59400    5
2640     4
44200    4
5940     4
4320     4
        ..
87100    1
6240     1
66600    1
70920    1
67500    1
Name: count, Length: 764, dtype: int64


injury_claim
0        25
640       7
480       7
6340      5
580       5
         ..
14840     1
6580      1
11820     1
16650     1
7500      1
Name: count, Length: 639, dtype: int64


property_claim
0        19
860       6
480       5
660       5
10000     5
         ..
3590      1
6480      1
4580      1
4920      1
7500      1
Name: count, Length: 626, dtype: int64


vehicle_claim
5040     7
3360     6
44800    5
3600     5
33600    5
        ..
43360    1
25130    1
38940    1
47430    1
52500    1
Name: count, Length: 727, dtype: int64


auto_make
Saab          80
Dodge         80
Suburu        80
Nissan        78
Chevrolet     76
Ford          72
BMW           72
Toyota        70
Audi          69
Accura        68
Volkswagen    68
Jeep          67
Mercedes      65
Honda         55
Name: count, dtype: int64


auto_model
RAM               43
Wrangler          42
A3                37
Neon              37
MDX               36
Jetta             35
Passat            33
A5                32
Legacy            32
Pathfinder        31
Malibu            30
92x               28
Camry             28
Forrestor         28
F150              27
95                27
E400              27
93                25
Grand Cherokee    25
Escape            24
Tahoe             24
Maxima            24
Ultima            23
X5                23
Highlander        22
Civic             22
Silverado         22
Fusion            21
ML350             20
Impreza           20
Corolla           20
TL                20
CRV               20
C300              18
3 Series          18
X6                16
M5                15
Accord            13
RSX               12
Name: count, dtype: int64


auto_year
1995    56
1999    55
2005    54
2006    53
2011    53
2007    52
2003    51
2010    50
2009    50
2013    49
2002    49
2015    47
2012    46
1997    46
2008    45
2014    44
2000    42
2001    42
1998    40
2004    38
1996    37
2004     1
Name: count, dtype: int64


fraud_reported
N    753
Y    247
Name: count, dtype: int64


ins_data.drop(['umbrella_limit' , 'insured_zip'] , axis = 1 , inplace = True)


ins_data['policy_bind_date']=pd.to_datetime(ins_data['policy_bind_date'])
ins_data['incident_date']=pd.to_datetime(ins_data['incident_date'])

<ipython-input-17-85a852d81472>:1: UserWarning: Parsing dates in %d-%m-%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.
  ins_data['policy_bind_date']=pd.to_datetime(ins_data['policy_bind_date'])
<ipython-input-17-85a852d81472>:2: UserWarning: Parsing dates in %d-%m-%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.
  ins_data['incident_date']=pd.to_datetime(ins_data['incident_date'])


# Extracting Day, Month and Year column from policy_bind_date
ins_data['policy_bind_day'] = ins_data['policy_bind_date'].dt.day
ins_data['policy_bind_month'] = ins_data['policy_bind_date'].dt.month
ins_data['policy_bind_year'] = ins_data['policy_bind_date'].dt.year

# Extracting Day, Month and Year column from incident_date
ins_data['incident_day'] = ins_data['incident_date'].dt.day
ins_data['incident_month'] = ins_data['incident_date'].dt.month
ins_data['incident_year'] = ins_data['incident_date'].dt.year


ins_data.drop(['policy_bind_date','incident_date'],axis=1,inplace=True)


ins_data[ins_data.columns[(ins_data=="?").any()]].nunique()

collision_type             4
property_damage            3
police_report_available    4
dtype: int64


ins_data['collision_type'].value_counts()

collision_type
Rear Collision     292
Side Collision     276
Front Collision    254
?                  178
Name: count, dtype: int64


ins_data['collision_type'] = ins_data.collision_type.str.replace('?' , ins_data['collision_type'].mode()[0])


ins_data['property_damage'].value_counts()

property_damage
?      360
NO     338
YES    302
Name: count, dtype: int64


ins_data['property_damage'] = ins_data.property_damage.str.replace('?' , 'NO')


ins_data['police_report_available'].value_counts()

police_report_available
?        343
NO       343
YES      313
YES.1      1
Name: count, dtype: int64


ins_data['police_report_available'] = ins_data.police_report_available.str.replace('?' , "NO")


# Extracting csl_per_person and cls_per_accident from policy_csl column.

ins_data['csl_per_person'] = ins_data.policy_csl.str.split('/' , expand=True)[0]
ins_data['csl_per_accident'] = ins_data.policy_csl.str.split('/' , expand = True)[1]


# Converting object datatype into integer data type
ins_data['csl_per_person']=ins_data['csl_per_person'].astype('int64')
ins_data['csl_per_accident']=ins_data['csl_per_accident'].astype('int64')


# since we ectracted the data from policy_csl column , So let's rop it.

ins_data.drop('policy_csl' , axis = 1 , inplace = True)


ins_data['auto_year'] = ins_data['auto_year'].astype('int64')


# Lets extract age of the vehicle from auto_year by subtracting it from the year 2018
ins_data['Vehicle_Age']=2021-ins_data['auto_year']
ins_data.drop("auto_year",axis=1,inplace=True)


# checking for unique value present in dataset

ins_data.nunique().to_frame("Number of unique values")


# Dropping incident_year column

ins_data.drop('incident_year' , axis = 1 , inplace = True)


ins_data.head()


ins_data.shape

(1000, 39)


ins_data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1000 entries, 0 to 999
Data columns (total 39 columns):
 #   Column                       Non-Null Count  Dtype 
---  ------                       --------------  ----- 
 0   months_as_customer           1000 non-null   object
 1   age                          1000 non-null   object
 2   policy_state                 1000 non-null   object
 3   policy_deductible            1000 non-null   object
 4   policy_annual_premium        1000 non-null   object
 5   insured_sex                  1000 non-null   object
 6   insured_education_level      1000 non-null   object
 7   insured_occupation           1000 non-null   object
 8   insured_hobbies              1000 non-null   object
 9   insured_relationship         1000 non-null   object
 10  capital-gains                1000 non-null   object
 11  capital-loss                 1000 non-null   object
 12  incident_type                1000 non-null   object
 13  collision_type               1000 non-null   object
 14  incident_severity            1000 non-null   object
 15  authorities_contacted        1000 non-null   object
 16  incident_state               1000 non-null   object
 17  incident_city                1000 non-null   object
 18  incident_hour_of_the_day     1000 non-null   object
 19  number_of_vehicles_involved  1000 non-null   object
 20  property_damage              1000 non-null   object
 21  bodily_injuries              1000 non-null   object
 22  witnesses                    1000 non-null   object
 23  police_report_available      1000 non-null   object
 24  total_claim_amount           1000 non-null   object
 25  injury_claim                 1000 non-null   object
 26  property_claim               1000 non-null   object
 27  vehicle_claim                1000 non-null   object
 28  auto_make                    1000 non-null   object
 29  auto_model                   1000 non-null   object
 30  fraud_reported               1000 non-null   object
 31  policy_bind_day              1000 non-null   int32 
 32  policy_bind_month            1000 non-null   int32 
 33  policy_bind_year             1000 non-null   int32 
 34  incident_day                 1000 non-null   int32 
 35  incident_month               1000 non-null   int32 
 36  csl_per_person               1000 non-null   int64 
 37  csl_per_accident             1000 non-null   int64 
 38  Vehicle_Age                  1000 non-null   int64 
dtypes: int32(5), int64(3), object(31)
memory usage: 293.0+ KB


ins_data['months_as_customer'] = ins_data['months_as_customer'].astype('int64')


ins_data['age'] = ins_data['age'].astype("int64")
ins_data['policy_deductible'] = ins_data['policy_deductible'].astype("int64")
ins_data['capital-gains'] = ins_data['capital-gains'].astype('int64')
ins_data['number_of_vehicles_involved'] = ins_data['number_of_vehicles_involved'].astype('int64')
ins_data['incident_hour_of_the_day'] = ins_data['incident_hour_of_the_day'].astype("int64")
ins_data['witnesses'] = ins_data['witnesses'].astype("int64")
ins_data['injury_claim'] = ins_data['injury_claim'].astype('int64')
ins_data['property_claim'] = ins_data['property_claim'].astype('int64')
ins_data['vehicle_claim'] = ins_data['vehicle_claim'].astype('int64')
ins_data['policy_bind_day'] = ins_data['policy_bind_day'].astype('int64')
ins_data['policy_bind_month'] = ins_data['policy_bind_month'].astype('int64')
ins_data['policy_bind_year'] = ins_data['policy_bind_year'].astype('int64')
ins_data['incident_month'] = ins_data['incident_month'].astype('int64')
ins_data['Vehicle_Age'] = ins_data['Vehicle_Age'].astype('int64')


# Convert the 'bodily_injuries' column to integers, ignoring any non-integer values

ins_data['total_claim_amount'] = ins_data['total_claim_amount'].astype('int64')
ins_data['bodily_injuries'] = ins_data['bodily_injuries'].astype('int64', errors='ignore') # Convert to int, ignoring errors
ins_data['capital-loss'] = ins_data['capital-loss'].astype("int64" , errors='ignore')


ins_data.info('policy_annual_premium')

<class 'pandas.core.frame.DataFrame'>
Index: 1000 entries, 0 to 999
Data columns (total 39 columns):
 #   Column                       Non-Null Count  Dtype 
---  ------                       --------------  ----- 
 0   months_as_customer           1000 non-null   int64 
 1   age                          1000 non-null   int64 
 2   policy_state                 1000 non-null   object
 3   policy_deductible            1000 non-null   int64 
 4   policy_annual_premium        1000 non-null   object
 5   insured_sex                  1000 non-null   object
 6   insured_education_level      1000 non-null   object
 7   insured_occupation           1000 non-null   object
 8   insured_hobbies              1000 non-null   object
 9   insured_relationship         1000 non-null   object
 10  capital-gains                1000 non-null   int64 
 11  capital-loss                 1000 non-null   object
 12  incident_type                1000 non-null   object
 13  collision_type               1000 non-null   object
 14  incident_severity            1000 non-null   object
 15  authorities_contacted        1000 non-null   object
 16  incident_state               1000 non-null   object
 17  incident_city                1000 non-null   object
 18  incident_hour_of_the_day     1000 non-null   int64 
 19  number_of_vehicles_involved  1000 non-null   int64 
 20  property_damage              1000 non-null   object
 21  bodily_injuries              1000 non-null   object
 22  witnesses                    1000 non-null   int64 
 23  police_report_available      1000 non-null   object
 24  total_claim_amount           1000 non-null   int64 
 25  injury_claim                 1000 non-null   int64 
 26  property_claim               1000 non-null   int64 
 27  vehicle_claim                1000 non-null   int64 
 28  auto_make                    1000 non-null   object
 29  auto_model                   1000 non-null   object
 30  fraud_reported               1000 non-null   object
 31  policy_bind_day              1000 non-null   int64 
 32  policy_bind_month            1000 non-null   int64 
 33  policy_bind_year             1000 non-null   int64 
 34  incident_day                 1000 non-null   int32 
 35  incident_month               1000 non-null   int64 
 36  csl_per_person               1000 non-null   int64 
 37  csl_per_accident             1000 non-null   int64 
 38  Vehicle_Age                  1000 non-null   int64 
dtypes: int32(1), int64(18), object(20)
memory usage: 308.6+ KB


ins_data['policy_annual_premium'] = ins_data['policy_annual_premium'].astype("int64" , errors = 'ignore')


# Checking for categorical columns :
cat_col = []
for col in ins_data.dtypes.index :
  if ins_data.dtypes[col] == 'object' :
    cat_col.append(col)
print("Categorical columns are : \n" , cat_col)
print("\n")

# Checking for numerical columns
numerical_col = []
for i in ins_data.dtypes.index:
    if ins_data.dtypes[i]!='object':
        numerical_col.append(i)
print("Numerical columns are:\n",numerical_col)
print("\n")

Categorical columns are : 
 ['policy_state', 'policy_annual_premium', 'insured_sex', 'insured_education_level', 'insured_occupation', 'insured_hobbies', 'insured_relationship', 'capital-loss', 'incident_type', 'collision_type', 'incident_severity', 'authorities_contacted', 'incident_state', 'incident_city', 'property_damage', 'bodily_injuries', 'police_report_available', 'auto_make', 'auto_model', 'fraud_reported']


Numerical columns are:
 ['months_as_customer', 'age', 'policy_deductible', 'capital-gains', 'incident_hour_of_the_day', 'number_of_vehicles_involved', 'witnesses', 'total_claim_amount', 'injury_claim', 'property_claim', 'vehicle_claim', 'policy_bind_day', 'policy_bind_month', 'policy_bind_year', 'incident_day', 'incident_month', 'csl_per_person', 'csl_per_accident', 'Vehicle_Age']


# checking for unique values in target columns :
ins_data['fraud_reported'].unique()

array(['Y', 'N'], dtype=object)


# Checking the list counts of target

ins_data['fraud_reported'].value_counts()

fraud_reported
N    753
Y    247
Name: count, dtype: int64


ins_data.describe()


# Visualizing how many insurance claims is fraudulent.

print(ins_data['fraud_reported'].value_counts())
sns.countplot(x = ins_data['fraud_reported'] , palette = 'Set3')
plt.show()

fraud_reported
N    753
Y    247
Name: count, dtype: int64

<ipython-input-47-61ed9e3952b8>:4: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = ins_data['fraud_reported'] , palette = 'Set3')


# Visualizing how many policy states are present in the dataset.

print(ins_data['policy_state'].value_counts())
sns.countplot(x = ins_data['policy_state'] , palette = 'PuRd')
plt.show()

policy_state
OH    352
IL    338
IN    310
Name: count, dtype: int64

<ipython-input-48-110852217e78>:4: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = ins_data['policy_state'] , palette = 'PuRd')


# Visualizing count of customers insured education level are present in the dataset
print(ins_data['insured_education_level'].value_counts())
sns.countplot(x = ins_data['insured_education_level'],palette='PuRd')
plt.show()

insured_education_level
JD             161
High School    160
Associate      145
MD             144
Masters        143
PhD            125
College        122
Name: count, dtype: int64

<ipython-input-49-2cff38011a92>:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = ins_data['insured_education_level'],palette='PuRd')


# Visualizing count of customers insured occupation are present in the dataset
print(ins_data['insured_occupation'].value_counts())
sns.countplot(x = ins_data['insured_occupation'],palette='RdBu')
plt.xticks(rotation=90)
plt.show()

insured_occupation
machine-op-inspct    93
prof-specialty       85
tech-support         78
sales                76
exec-managerial      76
craft-repair         74
transport-moving     72
other-service        71
priv-house-serv      71
armed-forces         69
adm-clerical         65
protective-serv      63
handlers-cleaners    54
farming-fishing      53
Name: count, dtype: int64

<ipython-input-50-d2c2f57722ab>:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = ins_data['insured_occupation'],palette='RdBu')


# Visualizing count of insured sex are present in the dataset
print(ins_data['insured_hobbies'].value_counts())
sns.countplot(x = ins_data['insured_hobbies'],palette='PuBu')
plt.xticks(rotation =90)
plt.show()

insured_hobbies
reading           64
exercise          57
paintball         57
bungie-jumping    56
movies            55
golf              55
camping           55
kayaking          54
yachting          53
hiking            52
video-games       50
skydiving         49
base-jumping      49
board-games       48
polo              47
chess             46
dancing           43
sleeping          41
cross-fit         35
basketball        34
Name: count, dtype: int64

<ipython-input-51-b8c66a2fd3d0>:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = ins_data['insured_hobbies'],palette='PuBu')


# Visualizing count of what kind of relationship the isured cutomers are in the dataset
print(ins_data['insured_relationship'].value_counts())
sns.countplot(x = ins_data['insured_relationship'],palette='PRGn')
plt.xticks(rotation = 45)
plt.show()

insured_relationship
own-child         183
other-relative    177
not-in-family     174
husband           170
wife              155
unmarried         141
Name: count, dtype: int64

<ipython-input-52-9bfd221db3fb>:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = ins_data['insured_relationship'],palette='PRGn')


# Visualizing count of insured sex are present in the dataset
print(ins_data['incident_type'].value_counts())
sns.countplot(x = ins_data['incident_type'],palette='Spectral')
plt.xticks(rotation = 45)
plt.show()

incident_type
Multi-vehicle Collision     419
Single Vehicle Collision    403
Vehicle Theft                94
Parked Car                   84
Name: count, dtype: int64

<ipython-input-53-aed4044a4c24>:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = ins_data['incident_type'],palette='Spectral')


# Visualizing count of insured sex are present in the dataset
print(ins_data['collision_type'].value_counts())
sns.countplot(x = ins_data['collision_type'],palette='OrRd')
plt.show()

collision_type
Rear Collision     470
Side Collision     276
Front Collision    254
Name: count, dtype: int64

<ipython-input-54-732d5b267353>:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = ins_data['collision_type'],palette='OrRd')


plt.figure(figsize = (25 , 35) , facecolor = 'white')
plotnumber = 1
for col in numerical_col :
  if plotnumber<=23 :
    ax = plt.subplot(8,3,plotnumber)
    sns.distplot(ins_data[col] , hist=False)
    plt.xlabel(col , fontsize = 20)
  plotnumber+=1
plt.show()

<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)
<ipython-input-55-f5ea0f468172>:6: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(ins_data[col] , hist=False)


# Comparing poicy_state and fraud_reported

plt.figure(figsize = (10 , 8))
sns.countplot(x = ins_data['policy_state'] , data = ins_data , hue = 'fraud_reported')
plt.title("POLICY_STATES vs FRAUD_REPORTED")
plt.show()


# Comparing insured_education_level and fraud_reported.

plt.figure(figsize = (10 , 8))
sns.countplot(x = ins_data['insured_education_level'] , data = ins_data , hue = ins_data['fraud_reported'])
plt.title("EDUCATION LEVEL vs FRAUD REPORTED")
plt.show()


# Comparing insured_occupation vs Fraud_reported.

plt.figure(figsize = (10 , 8))
sns.countplot(x = ins_data['insured_occupation'] , data = ins_data , hue = ins_data['fraud_reported'])
plt.xticks(rotation = 90)
plt.show()


# Comparing insured_hobbies and fraud_reported.

plt.figure(figsize = (10 , 8))
sns.countplot(x = ins_data['insured_hobbies'] , hue = ins_data['fraud_reported'] , data = ins_data)
plt.xticks(rotation = 90)
plt.show()


# Comparing insured_relationships and fraud_reported
sns.countplot(x = ins_data['insured_relationship'],data=ins_data,hue=ins_data['fraud_reported'],palette='Set2')
plt.xticks(rotation=90)
plt.show()


# Comparing incident_type and fraud_reported
sns.countplot(x = ins_data['incident_type'],data=ins_data,hue=ins_data['fraud_reported'],palette='Set2')
plt.xticks(rotation=45)
plt.show()


# Comparing collision_type and fraud_reported
sns.countplot(x = ins_data['collision_type'],data=ins_data,hue=ins_data['fraud_reported'],palette='Set2')
plt.show()


# Comparing incident_Severeity and fraud_reported
sns.countplot(x = ins_data['incident_severity'],data=ins_data, hue=ins_data['fraud_reported'] ,palette='Set2')
plt.show()


# Comparing authorities_contacted and fraud_reported
sns.countplot(x = ins_data['authorities_contacted'],data=ins_data,hue=ins_data['fraud_reported'],palette='Set1')
plt.show()


# Comparing incident_state and fraud_reported
sns.catplot(x=ins_data['incident_state'],data=ins_data,kind='count',col=ins_data['fraud_reported'],palette='RdBu')
plt.show()

<ipython-input-65-c0849494b9e1>:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.catplot(x=ins_data['incident_state'],data=ins_data,kind='count',col=ins_data['fraud_reported'],palette='RdBu')


# Comparig incident_city and fraud_reported
sns.catplot(x = ins_data['incident_city'],kind='count',data=ins_data,hue=ins_data['fraud_reported'],palette="bright")
plt.xticks(rotation=90)
plt.show()


# Comparing property_damage and fraud_reported
sns.catplot(x = ins_data['property_damage'],kind='count',data=ins_data,hue=ins_data['fraud_reported'],palette="crest")
plt.show()


# Comparing police_report_available and fraud_reported
sns.catplot(x = ins_data['police_report_available'],kind='count',data=ins_data,hue=ins_data['fraud_reported'],palette="bright")
plt.show()


fig,axes=plt.subplots(2,2,figsize=(12,10))

# Comparing insured_sex and age
sns.violinplot(x=ins_data['insured_sex'],y=ins_data['age'],ax=axes[0,0],data=ins_data,hue=ins_data["fraud_reported"],split=True)

# Comparing policy_state and witnesses
sns.violinplot(x=ins_data['policy_state'],y=ins_data['witnesses'],ax=axes[0,1],data=ins_data,hue=ins_data["fraud_reported"],split=True)

# Comparing csl_per_accident and property_claim
sns.violinplot(x=ins_data['csl_per_accident'],y=ins_data['property_claim'],ax=axes[1,0],data=ins_data,hue=ins_data["fraud_reported"],split=True)

# Comparing csl_per_person and age
sns.violinplot(x=ins_data['csl_per_person'],y=ins_data['age'],ax=axes[1,1],data=ins_data,hue=ins_data["fraud_reported"],split=True)
plt.show()


sns.pairplot(ins_data,hue="fraud_reported")
plt.show()


for i in numerical_col :
  sns.boxplot(ins_data[i])
  plt.show()


features = ins_data[['age', 'policy_annual_premium', 'total_claim_amount', 'property_claim' , 'incident_month']]
# from scipy.stats import zscore
# z = np.abs(zscore(features))
# z

#features = ins_data[['age', 'policy_annual_premium', 'total_claim_amount', 'property_claim']]
# Convert all columns in features to numeric
features = features.apply(pd.to_numeric, errors='coerce')
from scipy.stats import zscore
z = np.abs(zscore(features))
z


new_data = ins_data[(z<3).all(axis=1)]
new_data.head()


print(ins_data.shape)
print(new_data.shape)

(1000, 39)
(996, 39)


data_loss = (1000-996)/1000*100
data_loss

0.4


new_data.drop(['policy_state'] , axis = 1)


new_data.columns

Index(['months_as_customer', 'age', 'policy_state', 'policy_deductible',
       'policy_annual_premium', 'insured_sex', 'insured_education_level',
       'insured_occupation', 'insured_hobbies', 'insured_relationship',
       'capital-gains', 'capital-loss', 'incident_type', 'collision_type',
       'incident_severity', 'authorities_contacted', 'incident_state',
       'incident_city', 'incident_hour_of_the_day',
       'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
       'witnesses', 'police_report_available', 'total_claim_amount',
       'injury_claim', 'property_claim', 'vehicle_claim', 'auto_make',
       'auto_model', 'fraud_reported', 'policy_bind_day', 'policy_bind_month',
       'policy_bind_year', 'incident_day', 'incident_month', 'csl_per_person',
       'csl_per_accident', 'Vehicle_Age'],
      dtype='object')


new_data.drop(['insured_occupation'] , axis = 1 , inplace = True)

<ipython-input-78-61a412b4c491>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  new_data.drop(['insured_occupation'] , axis = 1 , inplace = True)


new_data.drop(['policy_state' , 'insured_sex', 'insured_education_level','incident_type', 'collision_type',
       'incident_severity', 'authorities_contacted', 'incident_state',
       'incident_city','property_damage','police_report_available','auto_make',
       'auto_model', 'fraud_reported','incident_day'], axis = 1 , inplace = True)

<ipython-input-79-a6694e5b9200>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  new_data.drop(['policy_state' , 'insured_sex', 'insured_education_level','incident_type', 'collision_type',


new_data.drop(['insured_hobbies'] , axis = 1 , inplace = True)

<ipython-input-81-f9b7cd1bce34>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  new_data.drop(['insured_hobbies'] , axis = 1 , inplace = True)


new_data.drop(['insured_relationship'] , axis = True , inplace = True)

<ipython-input-83-1f36aa198447>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  new_data.drop(['insured_relationship'] , axis = True , inplace = True)


new_data.skew()

months_as_customer             0.359605
age                            0.474526
policy_deductible              0.473229
policy_annual_premium          0.032042
capital-gains                   0.47885
capital-loss                  -0.393015
incident_hour_of_the_day      -0.039123
number_of_vehicles_involved    0.500364
bodily_injuries                 0.01075
witnesses                      0.025758
total_claim_amount            -0.593473
injury_claim                    0.26797
property_claim                  0.35713
vehicle_claim                 -0.619755
policy_bind_day                0.057251
policy_bind_month             -0.021269
policy_bind_year               0.058499
incident_month                 0.260283
csl_per_person                 0.413713
csl_per_accident               0.609316
Vehicle_Age                    0.049276
dtype: object


skew = ["total_claim_amount","vehicle_claim","incident_month","csl_per_accident"]

from sklearn.preprocessing import PowerTransformer
scaler = PowerTransformer(method='yeo-johnson')


new_data[skew] = scaler.fit_transform(new_data[skew].values)
new_data[skew].head()

<ipython-input-86-95388893c45a>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  new_data[skew] = scaler.fit_transform(new_data[skew].values)


new_data.skew()

months_as_customer             0.359605
age                            0.474526
policy_deductible              0.473229
policy_annual_premium          0.032042
capital-gains                   0.47885
capital-loss                  -0.393015
incident_hour_of_the_day      -0.039123
number_of_vehicles_involved    0.500364
bodily_injuries                 0.01075
witnesses                      0.025758
total_claim_amount            -0.508953
injury_claim                    0.26797
property_claim                  0.35713
vehicle_claim                 -0.521354
policy_bind_day                0.057251
policy_bind_month             -0.021269
policy_bind_year               0.058499
incident_month                 0.107284
csl_per_person                 0.413713
csl_per_accident               0.110964
Vehicle_Age                    0.049276
dtype: object


for i in new_data[skew] :
  sns.distplot(new_data[i])
  plt.show()

<ipython-input-88-3881a75cd7dd>:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(new_data[i])

<ipython-input-88-3881a75cd7dd>:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(new_data[i])

<ipython-input-88-3881a75cd7dd>:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(new_data[i])

<ipython-input-88-3881a75cd7dd>:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(new_data[i])


new_data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 996 entries, 0 to 999
Data columns (total 21 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   months_as_customer           996 non-null    int64  
 1   age                          996 non-null    int64  
 2   policy_deductible            996 non-null    int64  
 3   policy_annual_premium        996 non-null    object 
 4   capital-gains                996 non-null    int64  
 5   capital-loss                 996 non-null    object 
 6   incident_hour_of_the_day     996 non-null    int64  
 7   number_of_vehicles_involved  996 non-null    int64  
 8   bodily_injuries              996 non-null    object 
 9   witnesses                    996 non-null    int64  
 10  total_claim_amount           996 non-null    float64
 11  injury_claim                 996 non-null    int64  
 12  property_claim               996 non-null    int64  
 13  vehicle_claim                996 non-null    float64
 14  policy_bind_day              996 non-null    int64  
 15  policy_bind_month            996 non-null    int64  
 16  policy_bind_year             996 non-null    int64  
 17  incident_month               996 non-null    float64
 18  csl_per_person               996 non-null    int64  
 19  csl_per_accident             996 non-null    float64
 20  Vehicle_Age                  996 non-null    int64  
dtypes: float64(4), int64(14), object(3)
memory usage: 171.2+ KB


from sklearn.preprocessing import LabelEncoder

Le = LabelEncoder()
# iterate through each column in the cat_col list
for col in cat_col:
  # check if the column is of object type (string)
  if ins_data[col].dtype == 'object':
    # if it is, apply the LabelEncoder to that column
    # Convert the column to strings before applying LabelEncoder
    ins_data[col] = ins_data[col].astype(str)
    ins_data[col] = Le.fit_transform(ins_data[col])
ins_data[cat_col].head()


corr = new_data.corr()
corr


# Visualizing by Heatmap

plt.figure(figsize = (35,35))
sns.heatmap(corr , annot = True , fmt = '0.2f')
plt.show()


x = ins_data.drop(['fraud_reported'] , axis = 1)
y = ins_data['fraud_reported']


from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x = pd.DataFrame(scaler.fit_transform(x) , columns = x.columns)
x.head()


from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = pd.DataFrame()
vif["VIF values"] = [variance_inflation_factor(x.values,i) for i in range(len(x.columns))]
vif["Features"] = x.columns
vif

/usr/local/lib/python3.10/dist-packages/statsmodels/stats/outliers_influence.py:197: RuntimeWarning: divide by zero encountered in scalar divide
  vif = 1. / (1. - r_squared_i)


x.drop(['total_claim_amount'] , axis = 1 , inplace = True)


vif = pd.DataFrame()
vif["VIF values"] = [variance_inflation_factor(x.values,i) for i in range(len(x.columns))]
vif["Features"] = x.columns
vif


x.drop(['csl_per_person'] , axis = 1 , inplace = True)


vif = pd.DataFrame()
vif["VIF values"] = [variance_inflation_factor(x.values,i) for i in range(len(x.columns))]
vif["Features"] = x.columns
vif


y.value_counts()

fraud_reported
0    753
1    247
Name: count, dtype: int64


from imblearn.over_sampling import SMOTE
SM = SMOTE()
x , y = SM.fit_resample(x , y)


y.value_counts()

fraud_reported
1    753
0    753
Name: count, dtype: int64


from sklearn.model_selection import train_test_split as TTS
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

maxAccu = 0
maxRS = 0
for i in range(1,200) :
  x_train , x_test , y_train , y_test = TTS(x , y , test_size = 0.30 , random_state = i)
  RFC = RandomForestClassifier()
  RFC.fit(x_train , y_train)
  pred = RFC.predict(x_test)
  acc = accuracy_score(y_test , pred)
  if acc>maxAccu :
    maxAccu = acc
    maxRS = i
print("Best Accuracy Score is " , maxAccu , "at random state" , maxRS)

Best Accuracy Score is  0.918141592920354 at random state 15


x_train,x_test,y_train,y_test=TTS(x,y,test_size=.30,random_state=maxRS)


from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier, AdaBoostClassifier, BaggingClassifier
from xgboost import XGBClassifier as xgb
from sklearn.metrics import classification_report, confusion_matrix, roc_curve, accuracy_score
from sklearn.model_selection import cross_val_score


RFC = RandomForestClassifier()
RFC.fit(x_train,y_train)
predRFC = RFC.predict(x_test)
print(accuracy_score(y_test,predRFC))
print(confusion_matrix(y_test,predRFC))
print(classification_report(y_test,predRFC))

0.9137168141592921
[[200  17]
 [ 22 213]]
              precision    recall  f1-score   support

           0       0.90      0.92      0.91       217
           1       0.93      0.91      0.92       235

    accuracy                           0.91       452
   macro avg       0.91      0.91      0.91       452
weighted avg       0.91      0.91      0.91       452


cm = confusion_matrix(y_test,predRFC)

x_axis_labels = ["NO","YES"]
y_axis_labels = ["NO","YES"]

sns.heatmap(cm, annot = True, fmt = ".0f",xticklabels=x_axis_labels,yticklabels=y_axis_labels)

plt.xlabel("PREDICTED LABEL")
plt.ylabel("TRUE LABEL")
plt.title('Confusion Matrix for Random Forest Classifier')
plt.show()


DTC = DecisionTreeClassifier()
DTC.fit(x_train , y_train)
predDTC = DTC.predict(x_test)
print(accuracy_score(y_test , predDTC))
print(confusion_matrix(y_test , predDTC))
print(classification_report(y_test,predDTC))

0.838495575221239
[[178  39]
 [ 34 201]]
              precision    recall  f1-score   support

           0       0.84      0.82      0.83       217
           1       0.84      0.86      0.85       235

    accuracy                           0.84       452
   macro avg       0.84      0.84      0.84       452
weighted avg       0.84      0.84      0.84       452


cm = confusion_matrix(y_test,predDTC)

x_axis_labels = ["NO","YES"]
y_axis_labels = ["NO","YES"]

sns.heatmap(cm, annot = True,fmt = ".0f",xticklabels=x_axis_labels,yticklabels=y_axis_labels)

plt.xlabel("PREDICTED LABEL")
plt.ylabel("TRUE LABEL")
plt.title('Confusion Matrix for Decision Tree Classifier')
plt.show()


svc = SVC()
svc.fit(x_train,y_train)
predSVC = svc.predict(x_test)
print(accuracy_score(y_test , predSVC))
print(confusion_matrix(y_test , predSVC))
print(classification_report(y_test,predSVC))

0.8761061946902655
[[185  32]
 [ 24 211]]
              precision    recall  f1-score   support

           0       0.89      0.85      0.87       217
           1       0.87      0.90      0.88       235

    accuracy                           0.88       452
   macro avg       0.88      0.88      0.88       452
weighted avg       0.88      0.88      0.88       452


cm = confusion_matrix(y_test , predSVC)

x_axis_labels = ["NO" , "YES"]
y_axis_labels = ["NO" , "YES"]


sns.heatmap(cm, annot = True,fmt = ".0f",xticklabels=x_axis_labels,yticklabels=y_axis_labels)

plt.xlabel("PREDICTED LABEL")
plt.ylabel("TRUE LABEL")
plt.title('Confusion Matrix for Support Vector Machine Classifier')
plt.show()


GB = GradientBoostingClassifier()
GB.fit(x_train,y_train)
predGB = GB.predict(x_test)
print(accuracy_score(y_test,predGB))
print(confusion_matrix(y_test,predGB))
print(classification_report(y_test,predGB))

0.9048672566371682
[[194  23]
 [ 20 215]]
              precision    recall  f1-score   support

           0       0.91      0.89      0.90       217
           1       0.90      0.91      0.91       235

    accuracy                           0.90       452
   macro avg       0.90      0.90      0.90       452
weighted avg       0.90      0.90      0.90       452


cm = confusion_matrix(y_test , predGB)

x_axis_labels = ["NO" , "YES"]
y_axis_labels = ["NO" , "YES"]


sns.heatmap(cm, annot = True,fmt = ".0f",xticklabels=x_axis_labels,yticklabels=y_axis_labels)

plt.xlabel("PREDICTED LABEL")
plt.ylabel("TRUE LABEL")
plt.title('Confusion Matrix for Gradient Booster Classifier')
plt.show()


ABC = AdaBoostClassifier()
ABC.fit(x_train,y_train)
predABC = ABC.predict(x_test)
print(accuracy_score(y_test,predABC))
print(confusion_matrix(y_test,predABC))
print(classification_report(y_test,predABC))

0.9026548672566371
[[196  21]
 [ 23 212]]
              precision    recall  f1-score   support

           0       0.89      0.90      0.90       217
           1       0.91      0.90      0.91       235

    accuracy                           0.90       452
   macro avg       0.90      0.90      0.90       452
weighted avg       0.90      0.90      0.90       452


cm = confusion_matrix(y_test,predABC)

x_axis_labels = ["NO","YES"]
y_axis_labels = ["NO","YES"]

sns.heatmap(cm, annot = True, fmt = ".0f",xticklabels=x_axis_labels,yticklabels=y_axis_labels)

plt.xlabel("PREDICTED LABEL")
plt.ylabel("TRUE LABEL")
plt.title('Confusion Matrix for AdaBoost Classifier')
plt.show()


BC = BaggingClassifier()
BC.fit(x_train,y_train)
predBC = BC.predict(x_test)
print(accuracy_score(y_test,predBC))
print(confusion_matrix(y_test,predBC))
print(classification_report(y_test,predBC))

0.8960176991150443
[[202  15]
 [ 32 203]]
              precision    recall  f1-score   support

           0       0.86      0.93      0.90       217
           1       0.93      0.86      0.90       235

    accuracy                           0.90       452
   macro avg       0.90      0.90      0.90       452
weighted avg       0.90      0.90      0.90       452


cm = confusion_matrix(y_test,predBC)

x_axis_labels = ["NO","YES"]
y_axis_labels = ["NO","YES"]

sns.heatmap(cm, annot = True, fmt = ".0f", xticklabels=x_axis_labels,yticklabels=y_axis_labels)

plt.xlabel("PREDICTED LABEL")
plt.ylabel("TRUE LABEL")
plt.title('Confusion Matrix for Bagging Classifier')
plt.show()


ET = ExtraTreesClassifier()
ET.fit(x_train,y_train)
predET = ET.predict(x_test)
print(accuracy_score(y_test,predET))
print(confusion_matrix(y_test,predET))
print(classification_report(y_test,predET))

0.9336283185840708
[[200  17]
 [ 13 222]]
              precision    recall  f1-score   support

           0       0.94      0.92      0.93       217
           1       0.93      0.94      0.94       235

    accuracy                           0.93       452
   macro avg       0.93      0.93      0.93       452
weighted avg       0.93      0.93      0.93       452


cm = confusion_matrix(y_test,predET)

x_axis_labels = ["NO","YES"]
y_axis_labels = ["NO","YES"]

sns.heatmap(cm, annot = True, fmt = ".0f",xticklabels=x_axis_labels,yticklabels=y_axis_labels)

plt.xlabel("PREDICTED LABEL")
plt.ylabel("TRUE LABEL")
plt.title('Confusion Matrix for ExtraTrees Classifier')
plt.show()


XGB = xgb(verbosity=0)
XGB.fit(x_train,y_train)
predXGB = XGB.predict(x_test)
print(accuracy_score(y_test,predXGB))
print(confusion_matrix(y_test,predXGB))
print(classification_report(y_test,predXGB))

0.9247787610619469
[[195  22]
 [ 12 223]]
              precision    recall  f1-score   support

           0       0.94      0.90      0.92       217
           1       0.91      0.95      0.93       235

    accuracy                           0.92       452
   macro avg       0.93      0.92      0.92       452
weighted avg       0.93      0.92      0.92       452


cm = confusion_matrix(y_test,predXGB)

x_axis_labels = ["NO","YES"]
y_axis_labels = ["NO","YES"]

sns.heatmap(cm, annot = True, fmt = ".0f",xticklabels=x_axis_labels,yticklabels=y_axis_labels)

plt.xlabel("PREDICTED LABEL")
plt.ylabel("TRUE LABEL")
plt.title('Confusion Matrix for XG Boost Classifier')
plt.show()


from sklearn.model_selection import cross_val_score


print("Cross_Validation_Score using Random Forest Classifier:",cross_val_score(RFC,x,y,cv=5).mean())

Cross_Validation_Score using Random Forest Classifier: 0.8779124771732196


print("Cross_Validation_Score using Decision Tree Classifier:",cross_val_score(DTC,x,y,cv=5).mean())

Cross_Validation_Score using Decision Tree Classifier: 0.829429495500649


print("Cross_Validation_Score using Support Vector Classifier:",cross_val_score(svc,x,y,cv=5).mean())

Cross_Validation_Score using Support Vector Classifier: 0.8606059272623264


print("Cross_Validation_Score using Gradient Boosting Classifier:",cross_val_score(GB,x,y,cv=5).mean())

Cross_Validation_Score using Gradient Boosting Classifier: 0.8706013069019385


print("Cross_Validation_Score using AdaBoosting Classifier:",cross_val_score(ABC,x,y,cv=5).mean())

Cross_Validation_Score using AdaBoosting Classifier: 0.8666343974830036


print("Cross_Validation_Score using Bagging Classifier:",cross_val_score(BC,x,y,cv=5).mean())

Cross_Validation_Score using Bagging Classifier: 0.8573298717299949


print("Cross_Validation_Score using ExtraTrees Classifier:",cross_val_score(ET,x,y,cv=5).mean())

Cross_Validation_Score using ExtraTrees Classifier: 0.9110646630437174


print("Cross_Validation_Score using XG Boost Classifier:",cross_val_score(XGB,x,y,cv=5).mean())

Cross_Validation_Score using XG Boost Classifier: 0.8891795560053684


from sklearn.model_selection import GridSearchCV

parameters = {'criterion':['gini','entrophy'],
             'max_features':['auto','sqrt','log2'],
             'max_depth':[0,10,20],
             'n_jobs':[-2,-1,1],
             'n_estimators':[50,100,200,300]}


GCV = GridSearchCV(ExtraTreesClassifier(),parameters,cv=5)


GCV.fit(x_train,y_train)

/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(
/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: 
720 fits failed out of a total of 1080.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
180 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py", line 340, in fit
    self._validate_params()
  File "/usr/local/lib/python3.10/dist-packages/sklearn/base.py", line 600, in _validate_params
    validate_parameter_constraints(
  File "/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py", line 97, in validate_parameter_constraints
    raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'max_depth' parameter of ExtraTreesClassifier must be an int in the range [1, inf) or None. Got 0 instead.

--------------------------------------------------------------------------------
540 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py", line 340, in fit
    self._validate_params()
  File "/usr/local/lib/python3.10/dist-packages/sklearn/base.py", line 600, in _validate_params
    validate_parameter_constraints(
  File "/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py", line 97, in validate_parameter_constraints
    raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'criterion' parameter of ExtraTreesClassifier must be a str among {'entropy', 'log_loss', 'gini'}. Got 'entrophy' instead.

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
 0.86907696 0.86716768 0.86337621 0.87287294 0.87002031 0.86811555
 0.87951704 0.8728594  0.87097721 0.87476868 0.86908147 0.87192056
 0.87096818 0.87285489 0.86905439 0.87002031 0.86811104 0.86717671
 0.87096818 0.8633717  0.87382081 0.87381178 0.86907244 0.86907244
 0.86906793 0.87286843 0.86908599 0.86812458 0.87287294 0.86620627
 0.87665538 0.87571654 0.87476416 0.87096818 0.87192959 0.87476868
 0.88235613 0.88329497 0.88235161 0.87001128 0.88138569 0.88045137
 0.88804333 0.8889912  0.88329045 0.88995261 0.8861476  0.88045588
 0.87382081 0.87002483 0.87569849 0.88237418 0.87571654 0.88519973
 0.88426089 0.88141277 0.88236064 0.88046039 0.88425638 0.87570752
 0.87666892 0.87950801 0.87855563 0.88330399 0.88045588 0.8823471
 0.87950801 0.8823471  0.88044234 0.88046491 0.88425638 0.88804784
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan
        nan        nan        nan        nan        nan        nan]
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(

GridSearchCV(cv=5, estimator=ExtraTreesClassifier(),
             param_grid={'criterion': ['gini', 'entrophy'],
                         'max_depth': [0, 10, 20],
                         'max_features': ['auto', 'sqrt', 'log2'],
                         'n_estimators': [50, 100, 200, 300],
                         'n_jobs': [-2, -1, 1]})

GridSearchCV(cv=5, estimator=ExtraTreesClassifier(),
             param_grid={'criterion': ['gini', 'entrophy'],
                         'max_depth': [0, 10, 20],
                         'max_features': ['auto', 'sqrt', 'log2'],
                         'n_estimators': [50, 100, 200, 300],
                         'n_jobs': [-2, -1, 1]})

ExtraTreesClassifier()

ExtraTreesClassifier()


GCV.best_params_

{'criterion': 'gini',
 'max_depth': 20,
 'max_features': 'auto',
 'n_estimators': 300,
 'n_jobs': -2}


Insurance_model =  ExtraTreesClassifier(criterion='gini',max_features='auto',max_depth=20,n_estimators=300,n_jobs=-2)
Insurance_model.fit(x_train,y_train)
pred = Insurance_model.predict(x_test)
acc = accuracy_score(y_test,pred)
print(acc*100)

/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
  warn(

92.47787610619469


import matplotlib.pyplot as plt
from sklearn import datasets, model_selection, metrics
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, BaggingClassifier
from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn.metrics import RocCurveDisplay

# Load dataset and split into training and testing sets
data = datasets.load_breast_cancer()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=15)

# Initialize models
ET = ExtraTreesClassifier(random_state=15)
RFC = RandomForestClassifier(random_state=15)
svc = SVC(kernel='linear', probability=True, random_state=15)
GB = GradientBoostingClassifier(random_state=15)
ABC = AdaBoostClassifier(random_state=15)
BC = BaggingClassifier(random_state=15)
XGB = XGBClassifier(random_state=15)

# Fit models
ET.fit(X_train, y_train)
RFC.fit(X_train, y_train)
svc.fit(X_train, y_train)
GB.fit(X_train, y_train)
ABC.fit(X_train, y_train)
BC.fit(X_train, y_train)
XGB.fit(X_train, y_train)

# Plot ROC curves using RocCurveDisplay
fig, ax = plt.subplots(figsize=(10, 10))
RocCurveDisplay.from_estimator(ET, X_test, y_test, ax=ax, name='Extra Trees')
RocCurveDisplay.from_estimator(RFC, X_test, y_test, ax=ax, name='Random Forest')
RocCurveDisplay.from_estimator(svc, X_test, y_test, ax=ax, name='SVC')
RocCurveDisplay.from_estimator(GB, X_test, y_test, ax=ax, name='Gradient Boosting')
RocCurveDisplay.from_estimator(ABC, X_test, y_test, ax=ax, name='AdaBoost')
RocCurveDisplay.from_estimator(BC, X_test, y_test, ax=ax, name='Bagging')
RocCurveDisplay.from_estimator(XGB, X_test, y_test, ax=ax, name='XGBoost')

# Customize and show plot
plt.legend(prop={'size':11}, loc='lower right')
plt.title('AUC ROC Curves')
plt.show()


# Load dataset and split into training and testing sets
data = datasets.load_breast_cancer()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=15)

# Initialize models
ET = ExtraTreesClassifier(random_state=15)
RFC = RandomForestClassifier(random_state=15)
svc = SVC(kernel='linear', probability=True, random_state=15)
GB = GradientBoostingClassifier(random_state=15)
ABC = AdaBoostClassifier(random_state=15)
BC = BaggingClassifier(random_state=15)
XGB = XGBClassifier(random_state=15)

# Fit models
ET.fit(X_train, y_train)
RFC.fit(X_train, y_train)
svc.fit(X_train, y_train)
GB.fit(X_train, y_train)
ABC.fit(X_train, y_train)
BC.fit(X_train, y_train)
XGB.fit(X_train, y_train)

# Plot ROC curves using RocCurveDisplay
fig, ax = plt.subplots(figsize=(10, 10))
RocCurveDisplay.from_estimator(ET, X_test, y_test, ax=ax, name='Extra Trees')

<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x797d1e4e43d0>


import pickle
filename = 'insurance.pkl'
pickle.dump(RFC, open(filename, 'wb'))


prediction = Insurance_model.predict(x_test)
prediction

array([1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0,
       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,
       1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1,
       1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,
       1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1,
       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
       0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,
       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,
       1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1,
       1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
       1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
       1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,
       0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,
       1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
       1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0,
       0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1,
       0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,
       1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,
       0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0,
       0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0,
       1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1])


import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import ExtraTreesClassifier

# Load dataset
data = load_breast_cancer()
X, y = data.data, data.target

# Ensure that the split is correct with random_state=15
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=15)

# Train a model (using ExtraTreesClassifier as an example) with random_state=15
Insurance_model = ExtraTreesClassifier(random_state=15)
Insurance_model.fit(X_train, y_train)

# Make predictions
predictions = Insurance_model.predict(X_test)

# Ensure y_test is converted to a numpy array
a = np.array(y_test)

# Check lengths to ensure they match
print(f"Length of predictions: {len(predictions)}")
print(f"Length of y_test: {len(a)}")

# Create DataFrame
df = pd.DataFrame()
df["Predicted"] = predictions
df["Original"] = a

# Display DataFrame
print(df)

Length of predictions: 114
Length of y_test: 114
     Predicted  Original
0            1         1
1            1         1
2            0         0
3            1         1
4            1         1
..         ...       ...
109          0         0
110          1         1
111          0         0
112          0         0
113          1         1

[114 rows x 2 columns]

	months_as_customer	age	policy_deductible	capital-gains	incident_hour_of_the_day	number_of_vehicles_involved	witnesses	total_claim_amount	injury_claim	property_claim	vehicle_claim	policy_bind_day	policy_bind_month	policy_bind_year	incident_day	incident_month	csl_per_person	csl_per_accident	Vehicle_Age
count	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.00000	1000.000000	1000.00000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000
mean	203.954000	38.948000	1136.000000	25126.100000	11.644000	1.83900	1.487000	52761.94000	7433.420000	7399.570000	37928.950000	15.448000	6.559000	2001.604000	14.995000	1.496000	272.650000	580.200000	15.897000
std	115.113174	9.140287	611.864673	27872.187708	6.951373	1.01888	1.111335	26401.53319	4880.951853	4824.726179	18886.252893	8.808001	3.499824	7.360391	8.670995	0.523697	161.603196	287.420547	6.015861
min	0.000000	19.000000	500.000000	0.000000	0.000000	1.00000	0.000000	100.00000	0.000000	0.000000	70.000000	1.000000	1.000000	1990.000000	1.000000	1.000000	100.000000	300.000000	6.000000
25%	115.750000	32.000000	500.000000	0.000000	6.000000	1.00000	1.000000	41812.50000	4295.000000	4445.000000	30292.500000	8.000000	3.000000	1995.000000	7.750000	1.000000	100.000000	300.000000	11.000000
50%	199.500000	38.000000	1000.000000	0.000000	12.000000	1.00000	1.000000	58055.00000	6775.000000	6750.000000	42100.000000	16.000000	7.000000	2002.000000	15.000000	1.000000	250.000000	500.000000	16.000000
75%	276.250000	44.000000	2000.000000	51025.000000	17.000000	3.00000	2.000000	70592.50000	11305.000000	10885.000000	50822.500000	23.000000	10.000000	2008.000000	22.000000	2.000000	500.000000	1000.000000	21.000000
max	479.000000	64.000000	2000.000000	100500.000000	23.000000	4.00000	3.000000	114920.00000	21450.000000	23670.000000	79560.000000	31.000000	12.000000	2015.000000	31.000000	3.000000	500.000000	1000.000000	26.000000

	age	policy_annual_premium	total_claim_amount	property_claim	incident_month
0	0.990836	0.616705	0.714257	1.165505	0.947587
1	0.334073	0.242521	1.807312	1.372696	0.947587
2	1.088913	0.642233	0.686362	0.736072	0.962871
3	0.224613	0.652886	0.403135	0.219722	0.947587
4	0.552994	1.341980	1.753121	1.399654	0.962871
...	...	...	...	...	...
995	0.103769	0.222884	1.305049	0.273817	0.962871
996	0.224613	0.739141	2.111466	2.214794	0.947587
997	0.541611	0.520739	0.558507	0.020826	0.947587
998	2.523284	0.411866	0.219110	0.451976	0.962871
999	2.304363	2.008710	1.807691	1.343664	0.962871

	total_claim_amount	vehicle_claim	incident_month	csl_per_accident
0	0.717556	0.754553	-0.966028	0.052612
1	-1.777785	-1.787353	-0.966028	0.052612
2	-0.716483	-0.820820	0.996033	-1.174021
3	0.392931	0.678427	-0.966028	0.052612
4	-1.730555	-1.740710	0.996033	1.313327

	months_as_customer	age	policy_deductible	policy_annual_premium	capital-gains	capital-loss	incident_hour_of_the_day	number_of_vehicles_involved	bodily_injuries	witnesses	...	injury_claim	property_claim	vehicle_claim	policy_bind_day	policy_bind_month	policy_bind_year	incident_month	csl_per_person	csl_per_accident	Vehicle_Age
months_as_customer	1.000000	0.922092	0.023512	-0.003378	0.007601	0.020771	0.068962	0.013609	-0.010994	0.063530	...	0.066416	0.041157	0.062392	0.058051	-0.004430	-0.046010	-0.049327	-0.022063	-0.022928	-0.001287
age	0.922092	1.000000	0.026772	0.005890	-0.006333	0.008192	0.085509	0.021140	-0.017339	0.056658	...	0.075480	0.064230	0.062165	0.058530	0.005683	-0.035505	-0.042259	-0.012844	-0.012459	-0.003268
policy_deductible	0.023512	0.026772	1.000000	-0.008445	0.035950	-0.024018	0.059733	0.051317	-0.023412	0.069433	...	0.040337	0.069537	0.005902	0.001948	-0.026390	-0.048465	-0.030969	0.006634	0.006179	-0.027497
policy_annual_premium	-0.003378	0.005890	-0.008445	1.000000	-0.009757	0.029835	-0.003001	-0.044711	0.025399	0.006365	...	-0.024031	-0.007964	0.012961	-0.040755	-0.003040	0.001650	-0.048497	0.028569	0.030755	0.040439
capital-gains	0.007601	-0.006333	0.035950	-0.009757	1.000000	-0.049528	-0.018367	0.062047	0.053731	-0.017785	...	0.027397	-0.003947	0.015089	-0.004177	0.020270	0.041789	-0.036086	0.003241	0.004182	-0.031282
capital-loss	0.020771	0.008192	-0.024018	0.029835	-0.049528	1.000000	-0.028729	-0.013205	-0.027711	-0.041611	...	-0.041919	-0.023884	-0.030884	-0.023172	-0.029116	-0.025891	0.009385	-0.032792	-0.034327	0.056410
incident_hour_of_the_day	0.068962	0.085509	0.059733	-0.003001	-0.018367	-0.028729	1.000000	0.121555	-0.039657	0.009373	...	0.168915	0.179917	0.212529	-0.011552	0.007101	0.037596	-0.009485	-0.001586	-0.000614	-0.023178
number_of_vehicles_involved	0.013609	0.021140	0.051317	-0.044711	0.062047	-0.013205	0.121555	1.000000	0.014391	-0.011877	...	0.224700	0.221560	0.261934	-0.000295	-0.029637	0.028498	0.042683	-0.019306	-0.019764	-0.032343
bodily_injuries	-0.010994	-0.017339	-0.023412	0.025399	0.053731	-0.027711	-0.039657	0.014391	1.000000	-0.003377	...	0.048743	0.035491	0.043651	-0.044891	-0.028476	0.032093	-0.001354	0.001755	-0.000339	0.018459
witnesses	0.063530	0.056658	0.069433	0.006365	-0.017785	-0.041611	0.009373	-0.011877	-0.003377	1.000000	...	-0.025750	0.050073	-0.023812	-0.011447	0.023485	0.052651	0.015845	0.065395	0.069088	-0.046001
total_claim_amount	0.064662	0.070582	0.025169	0.002905	0.015032	-0.034401	0.215756	0.268557	0.047081	-0.012244	...	0.806404	0.813265	0.981240	0.010714	0.014147	-0.002566	-0.037774	-0.050838	-0.057052	0.034282
injury_claim	0.066416	0.075480	0.040337	-0.024031	0.027397	-0.041919	0.168915	0.224700	0.048743	-0.025750	...	1.000000	0.566266	0.720163	0.021118	-0.007999	0.001598	-0.023989	-0.074952	-0.080216	0.013204
property_claim	0.041157	0.064230	0.069537	-0.007964	-0.003947	-0.023884	0.179917	0.221560	0.035491	0.050073	...	0.566266	1.000000	0.731656	0.002745	0.023354	-0.002113	-0.078212	-0.042894	-0.047923	0.014117
vehicle_claim	0.062392	0.062165	0.005902	0.012961	0.015089	-0.030884	0.212529	0.261934	0.043651	-0.023812	...	0.720163	0.731656	1.000000	0.008744	0.015890	-0.003297	-0.025955	-0.040517	-0.046567	0.041049
policy_bind_day	0.058051	0.058530	0.001948	-0.040755	-0.004177	-0.023172	-0.011552	-0.000295	-0.044891	-0.011447	...	0.021118	0.002745	0.008744	1.000000	0.033069	0.006017	0.010397	0.004742	0.007449	-0.037054
policy_bind_month	-0.004430	0.005683	-0.026390	-0.003040	0.020270	-0.029116	0.007101	-0.029637	-0.028476	0.023485	...	-0.007999	0.023354	0.015890	0.033069	1.000000	-0.026337	0.076222	-0.024179	-0.020859	0.030788
policy_bind_year	-0.046010	-0.035505	-0.048465	0.001650	0.041789	-0.025891	0.037596	0.028498	0.032093	0.052651	...	0.001598	-0.002113	-0.003297	0.006017	-0.026337	1.000000	0.051920	-0.016414	-0.017605	-0.015600
incident_month	-0.049327	-0.042259	-0.030969	-0.048497	-0.036086	0.009385	-0.009485	0.042683	-0.001354	0.015845	...	-0.023989	-0.078212	-0.025955	0.010397	0.076222	0.051920	1.000000	-0.029793	-0.029173	0.012872
csl_per_person	-0.022063	-0.012844	0.006634	0.028569	0.003241	-0.032792	-0.001586	-0.019306	0.001755	0.065395	...	-0.074952	-0.042894	-0.040517	0.004742	-0.024179	-0.016414	-0.029793	1.000000	0.990224	0.037820
csl_per_accident	-0.022928	-0.012459	0.006179	0.030755	0.004182	-0.034327	-0.000614	-0.019764	-0.000339	0.069088	...	-0.080216	-0.047923	-0.046567	0.007449	-0.020859	-0.017605	-0.029173	0.990224	1.000000	0.035074
Vehicle_Age	-0.001287	-0.003268	-0.027497	0.040439	-0.031282	0.056410	-0.023178	-0.032343	0.018459	-0.046001	...	0.013204	0.014117	0.041049	-0.037054	0.030788	-0.015600	0.012872	0.037820	0.035074	1.000000

	months_as_customer	age	policy_state	policy_deductible	policy_annual_premium	insured_sex	insured_education_level	insured_occupation	insured_hobbies	insured_relationship	...	auto_make	auto_model	policy_bind_day	policy_bind_month	policy_bind_year	incident_day	incident_month	csl_per_person	csl_per_accident	Vehicle_Age
0	1.078140	0.990836	1.187173	-0.222383	0.296253	1.076953	0.530931	-1.158316	1.281737	-1.437957	...	0.856466	-1.645743	0.176292	0.983684	1.684992	1.154424	-0.947587	-0.140228	-0.279173	0.183440
1	0.208995	0.334073	-0.016856	1.412784	-0.879770	1.076953	0.530931	-0.162558	0.929078	-0.247595	...	0.360539	-0.657470	1.312191	-0.159802	0.597550	0.692885	-0.947587	-0.140228	-0.279173	-0.315491
2	-0.608002	-1.088913	1.187173	1.412784	0.324253	-0.928546	1.557877	1.082141	-1.363201	0.347586	...	-0.631315	0.959702	-1.073198	0.697813	-0.218032	0.808270	0.962871	-1.068892	-0.975366	-0.315491
3	0.452355	0.224613	-1.220886	1.412784	0.334753	-0.928546	1.557877	-1.407256	-1.363201	0.942766	...	-0.879279	1.319074	1.085011	-0.445674	-1.577335	-0.576347	-0.947587	-0.140228	-0.279173	-1.479664
4	0.208995	0.552994	-1.220886	-0.222383	0.891264	1.076953	-1.522961	1.082141	-1.363201	0.942766	...	-1.623169	1.049545	-1.073198	-0.159802	1.684992	0.231346	0.962871	1.407545	1.461308	-0.648112

DATA PRE-PROCESSING¶

To find where the ? data is present throughout the dataset¶

EDA : EXPLORATORY DATA ANALYSIS¶

Uni-Variate Analysis¶

Checking for the distribution of Skewness.¶

Bi-Variate Analysis¶

Multi-Variate Analysis¶

Checking for Outliers¶

Checking for Skewness ¶

Encoding the categorical Data ¶

Correlation¶

Standard Scaler¶

Checking for Multicolinearity¶

Oversampling¶

MODEL BUILDING¶

Random Forest Classifier¶

Decision Tree Classifier¶

Support Vector Machine Classifier¶

Gradient Booster Classifier¶

AdaBoost Classifier¶

Bagging Classifier¶

Extra Tree Classifier¶

XG BOOST CLASSIFIER¶

Checking Cross Validation Score¶

Best Model - ExtraTrees Classifiers¶

Hyperparameter Tuning¶

We get accuracy of 92.47% after hyperparameter tuning.¶

Plotting ROC and compare AUC for all the models used¶

Plotting ROC for the Best Model¶

Saving the Model¶

	328	48	521585	17-10-2014	OH	250/500	1000	1406.91	0	466132	...	2	YES.1	71610	6510	13020	52080	Saab	92x	2004	Y
0	228	42	342868	27-06-2006	IN	250/500	2000	1197.22	5000000	468176	...	0	?	5070	780	780	3510	Mercedes	E400	2007	Y
1	134	29	687698	06-09-2000	OH	100/300	2000	1413.14	5000000	430632	...	3	NO	34650	7700	3850	23100	Dodge	RAM	2007	N
2	256	41	227811	25-05-1990	IL	250/500	2000	1415.74	6000000	608117	...	2	NO	63400	6340	6340	50720	Chevrolet	Tahoe	2014	Y
3	228	44	367455	06-06-2014	IL	500/1000	1000	1583.91	6000000	610706	...	1	NO	6500	1300	650	4550	Accura	RSX	2009	N
4	256	39	104594	12-10-2006	OH	250/500	1000	1351.10	0	478456	...	2	NO	64100	6410	6410	51280	Saab	95	2003	Y
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
994	3	38	941851	16-07-1991	OH	500/1000	1000	1310.80	0	431289	...	1	?	87200	17440	8720	61040	Honda	Accord	2006	N
995	285	41	186934	05-01-2014	IL	100/300	1000	1436.79	0	608177	...	3	?	108480	18080	18080	72320	Volkswagen	Passat	2015	N
996	130	34	918516	17-02-2003	OH	250/500	500	1383.49	3000000	442797	...	3	YES	67500	7500	7500	52500	Suburu	Impreza	1996	N
997	458	62	533940	18-11-2011	IL	500/1000	2000	1356.92	5000000	441714	...	1	YES	46980	5220	5220	36540	Audi	A5	1998	N
998	456	60	556080	11-11-1996	OH	250/500	1000	766.19	0	612260	...	3	?	5060	460	920	3680	Mercedes	E400	2007	N

	months_as_customer	age	policy_state	policy_deductible	policy_annual_premium	insured_sex	insured_education_level	insured_occupation	insured_hobbies	insured_relationship	...	auto_model	fraud_reported	policy_bind_day	policy_bind_month	policy_bind_year	incident_day	incident_month	csl_per_person	csl_per_accident	Vehicle_Age
0	328	48	OH	1000	1406.91	MALE	MD	craft-repair	sleeping	husband	...	92x	Y	17	10	2014	25	1	250	500	17
1	228	42	IN	2000	1197.22	MALE	MD	machine-op-inspct	reading	other-relative	...	E400	Y	27	6	2006	21	1	250	500	14
2	134	29	OH	2000	1413.14	FEMALE	PhD	sales	board-games	own-child	...	RAM	N	6	9	2000	22	2	100	300	14
3	256	41	IL	2000	1415.74	FEMALE	PhD	armed-forces	board-games	unmarried	...	Tahoe	Y	25	5	1990	10	1	250	500	7
4	228	44	IL	1000	1583.91	MALE	Associate	sales	board-games	unmarried	...	RSX	N	6	6	2014	17	2	500	1000	12

	policy_state	policy_annual_premium	insured_sex	insured_education_level	insured_occupation	insured_hobbies	insured_relationship	capital-loss	incident_type	collision_type	incident_severity	authorities_contacted	incident_state	incident_city	property_damage	bodily_injuries	police_report_available	auto_make	auto_model	fraud_reported
0	2	579	1	4	2	17	0	354	2	2	0	3	4	1	1	2	2	10	1	1
1	1	243	1	4	6	15	2	353	3	1	1	3	5	5	0	0	0	8	12	1
2	2	587	0	6	11	2	3	353	0	1	1	3	1	1	0	3	0	4	30	0
3	0	590	0	6	1	2	4	253	2	0	0	3	2	0	0	1	0	3	34	1
4	0	749	1	0	11	2	4	139	3	1	1	3	1	0	0	0	0	0	31	0

	VIF values	Features
0	6.853153	months_as_customer
1	6.855520	age
2	1.034655	policy_state
3	1.048289	policy_deductible
4	1.039608	policy_annual_premium
5	1.033795	insured_sex
6	1.052832	insured_education_level
7	1.015486	insured_occupation
8	1.049817	insured_hobbies
9	1.046578	insured_relationship
10	1.035290	capital-gains
11	1.039916	capital-loss
12	5.148690	incident_type
13	1.044505	collision_type
14	1.200199	incident_severity
15	1.246954	authorities_contacted
16	1.037181	incident_state
17	1.028783	incident_city
18	1.107230	incident_hour_of_the_day
19	5.105718	number_of_vehicles_involved
20	1.024985	property_damage
21	1.025717	bodily_injuries
22	1.045827	witnesses
23	1.042742	police_report_available
24	inf	total_claim_amount
25	inf	injury_claim
26	inf	property_claim
27	inf	vehicle_claim
28	1.076855	auto_make
29	1.070399	auto_model
30	1.026138	policy_bind_day
31	1.033316	policy_bind_month
32	1.031816	policy_bind_year
33	1.049486	incident_day
34	1.069654	incident_month
35	96.955445	csl_per_person
36	96.874834	csl_per_accident
37	1.037631	Vehicle_Age

	VIF values	Features
0	6.849868	months_as_customer
1	6.852643	age
2	1.033748	policy_state
3	1.048272	policy_deductible
4	1.036757	policy_annual_premium
5	1.028707	insured_sex
6	1.052824	insured_education_level
7	1.015328	insured_occupation
8	1.048613	insured_hobbies
9	1.042029	insured_relationship
10	1.035250	capital-gains
11	1.037633	capital-loss
12	5.145129	incident_type
13	1.044240	collision_type
14	1.200198	incident_severity
15	1.245508	authorities_contacted
16	1.036617	incident_state
17	1.028329	incident_city
18	1.106731	incident_hour_of_the_day
19	5.103786	number_of_vehicles_involved
20	1.024914	property_damage
21	1.025456	bodily_injuries
22	1.044647	witnesses
23	1.038449	police_report_available
24	2.176013	injury_claim
25	2.268003	property_claim
26	3.498639	vehicle_claim
27	1.074585	auto_make
28	1.069246	auto_model
29	1.025824	policy_bind_day
30	1.032829	policy_bind_month
31	1.031779	policy_bind_year
32	1.049429	incident_day
33	1.069603	incident_month
34	1.027276	csl_per_accident
35	1.037536	Vehicle_Age

	policy_state	policy_annual_premium	insured_sex	insured_education_level	insured_occupation	insured_hobbies	insured_relationship	capital-loss	incident_type	collision_type	incident_severity	authorities_contacted	incident_state	incident_city	property_damage	bodily_injuries	police_report_available	auto_make	auto_model	fraud_reported
0	2	579	1	4	2	17	0	354	2	2	0	3	4	1	1	2	2	10	1	1
1	1	243	1	4	6	15	2	353	3	1	1	3	5	5	0	0	0	8	12	1
2	2	587	0	6	11	2	3	353	0	1	1	3	1	1	0	3	0	4	30	0
3	0	590	0	6	1	2	4	253	2	0	0	3	2	0	0	1	0	3	34	1
4	0	749	1	0	11	2	4	139	3	1	1	3	1	0	0	0	0	0	31	0

	policy_state	policy_annual_premium	insured_sex	insured_education_level	insured_occupation	insured_hobbies	insured_relationship	capital-loss	incident_type	collision_type	incident_severity	authorities_contacted	incident_state	incident_city	property_damage	bodily_injuries	police_report_available	auto_make	auto_model	fraud_reported
0	2	579	1	4	2	17	0	354	2	2	0	3	4	1	1	2	2	10	1	1
1	1	243	1	4	6	15	2	353	3	1	1	3	5	5	0	0	0	8	12	1
2	2	587	0	6	11	2	3	353	0	1	1	3	1	1	0	3	0	4	30	0
3	0	590	0	6	1	2	4	253	2	0	0	3	2	0	0	1	0	3	34	1
4	0	749	1	0	11	2	4	139	3	1	1	3	1	0	0	0	0	0	31	0